eprintid: 5758 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/57/58 datestamp: 2023-02-07 23:30:11 lastmod: 2023-02-07 23:30:11 status_changed: 2023-02-07 23:30:11 type: article metadata_visibility: show creators_name: Romero, Inés creators_name: Ochoa-Zezzati, Alberto title: Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector ispublished: pub subjects: uneat_eng divisions: uninimx_produccion_cientifica full_text_status: public abstract: Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker. date: 2022 publication: Journal of Electrical and Computer Engineering volume: 2022 pagerange: 1-12 id_number: doi:10.1155/2022/5193336 refereed: TRUE issn: 2090-0147 official_url: http://doi.org/10.1155/2022/5193336 access: open language: en citation: Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker. metadata Romero, Inés y Ochoa-Zezzati, Alberto mail SIN ESPECIFICAR (2022) Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector. Journal of Electrical and Computer Engineering, 2022. pp. 1-12. ISSN 2090-0147 document_url: http://repositorio.unini.edu.mx/id/eprint/5758/1/5193336.pdf